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BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer
Breast cancer is a disease with high heterogeneity. Many issues on tumorigenesis and progression are still elusive. It is critical to identify genes that play important roles in the progression of tumors, especially for tumors with poor prognosis such as basal-like breast cancer and tumors in very y...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361122/ https://www.ncbi.nlm.nih.gov/pubmed/28327601 http://dx.doi.org/10.1038/srep45235 |
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author | Wu, Jiaqi Hu, Shuofeng Chen, Yaowen Li, Zongcheng Zhang, Jian Yuan, Hanyu Shi, Qiang Shao, Ningsheng Ying, Xiaomin |
author_facet | Wu, Jiaqi Hu, Shuofeng Chen, Yaowen Li, Zongcheng Zhang, Jian Yuan, Hanyu Shi, Qiang Shao, Ningsheng Ying, Xiaomin |
author_sort | Wu, Jiaqi |
collection | PubMed |
description | Breast cancer is a disease with high heterogeneity. Many issues on tumorigenesis and progression are still elusive. It is critical to identify genes that play important roles in the progression of tumors, especially for tumors with poor prognosis such as basal-like breast cancer and tumors in very young women. To facilitate the identification of potential regulatory or driver genes, we present the Breast Cancer Integrative Platform (BCIP, http://omics.bmi.ac.cn/bcancer/). BCIP maintains multi-omics data selected with strict quality control and processed with uniform normalization methods, including gene expression profiles from 9,005 tumor and 376 normal tissue samples, copy number variation information from 3,035 tumor samples, microRNA-target interactions, co-expressed genes, KEGG pathways, and mammary tissue-specific gene functional networks. This platform provides a user-friendly interface integrating comprehensive and flexible analysis tools on differential gene expression, copy number variation, and survival analysis. The prominent characteristic of BCIP is that users can perform analysis by customizing subgroups with single or combined clinical features, including subtypes, histological grades, pathologic stages, metastasis status, lymph node status, ER/PR/HER2 status, TP53 mutation status, menopause status, age, tumor size, therapy responses, and prognosis. BCIP will help to identify regulatory or driver genes and candidate biomarkers for further research in breast cancer. |
format | Online Article Text |
id | pubmed-5361122 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53611222017-03-24 BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer Wu, Jiaqi Hu, Shuofeng Chen, Yaowen Li, Zongcheng Zhang, Jian Yuan, Hanyu Shi, Qiang Shao, Ningsheng Ying, Xiaomin Sci Rep Article Breast cancer is a disease with high heterogeneity. Many issues on tumorigenesis and progression are still elusive. It is critical to identify genes that play important roles in the progression of tumors, especially for tumors with poor prognosis such as basal-like breast cancer and tumors in very young women. To facilitate the identification of potential regulatory or driver genes, we present the Breast Cancer Integrative Platform (BCIP, http://omics.bmi.ac.cn/bcancer/). BCIP maintains multi-omics data selected with strict quality control and processed with uniform normalization methods, including gene expression profiles from 9,005 tumor and 376 normal tissue samples, copy number variation information from 3,035 tumor samples, microRNA-target interactions, co-expressed genes, KEGG pathways, and mammary tissue-specific gene functional networks. This platform provides a user-friendly interface integrating comprehensive and flexible analysis tools on differential gene expression, copy number variation, and survival analysis. The prominent characteristic of BCIP is that users can perform analysis by customizing subgroups with single or combined clinical features, including subtypes, histological grades, pathologic stages, metastasis status, lymph node status, ER/PR/HER2 status, TP53 mutation status, menopause status, age, tumor size, therapy responses, and prognosis. BCIP will help to identify regulatory or driver genes and candidate biomarkers for further research in breast cancer. Nature Publishing Group 2017-03-22 /pmc/articles/PMC5361122/ /pubmed/28327601 http://dx.doi.org/10.1038/srep45235 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Wu, Jiaqi Hu, Shuofeng Chen, Yaowen Li, Zongcheng Zhang, Jian Yuan, Hanyu Shi, Qiang Shao, Ningsheng Ying, Xiaomin BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer |
title | BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer |
title_full | BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer |
title_fullStr | BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer |
title_full_unstemmed | BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer |
title_short | BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer |
title_sort | bcip: a gene-centered platform for identifying potential regulatory genes in breast cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361122/ https://www.ncbi.nlm.nih.gov/pubmed/28327601 http://dx.doi.org/10.1038/srep45235 |
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